Database intrusion detection (DB-IDS) is the problem of detecting anomalous queries in transaction systems like e-commerce platform. The adaptive detection algorithm is necessary to find anomaly accesses when the environment changes continuously. To solve this problem, we used accuracy-based LCS (XCS), one of the primary model of adaptive machine learning method, for detecting malicious accesses in databases. In the problem of database intrusion detection which changes the detecting targets, we found and analyzed the patterns of rule generation to show systemically how the adaptive learning of XCS algorithm is working in practical usage.
|Title of host publication||GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||7|
|Publication status||Published - 2018 Jul 6|
|Event||2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan|
Duration: 2018 Jul 15 → 2018 Jul 19
|Name||GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion|
|Other||2018 Genetic and Evolutionary Computation Conference, GECCO 2018|
|Period||18/7/15 → 18/7/19|
Bibliographical notePublisher Copyright:
© 2018 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computational Theory and Mathematics
- Theoretical Computer Science